The time complexity of the greedy method is \(O(q \cdot m^2)\) , where the worst case occurs with a densely connected graph. In practice, edges are only sparsely connected (\(|E| \approx O(m)\) ), especially with a narrow kernel bandwidth \(K_{h_W}\) , leading to an average time complexity of \(O(q \cdot m)\) . Some of the previous studies [1]}, [2]}, [3]} developed a clustering-based or a distance-based anchor selection. Although they help to maximize the coverage of local models, these methods incur high computational overhead and low performance. By contrast, our coverage-based anchor selection chooses anchor users progressively at a low computational cost.
